Forecasting wind speed using empirical mode decomposition and Elman neural network |
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| Affiliation: | 1. School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing 210044, China;2. Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China;3. School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China;1. Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, PR China;2. National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, PR China;3. School of Instrument Science and Engineering, Southeast University, Nanjing 210096, PR China;1. Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China;2. National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China;3. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China;4. Department of Industrial Engineering, University of Houston, TX, United States;1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA;2. Department of Electrical Engineering, Yildiz Technical University, Istanbul, Turkey;1. Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China;2. College of Science, Nanjing University of Posts and Telecommunications, Nanjing 210046, China;3. Key Laboratory for Land Surface Process and Climate Change in Cold and Arid Regions, Chinese Academy of Sciences, Lanzhou 730000, China;4. School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China |
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| Abstract: | Because of the chaotic nature and intrinsic complexity of wind speed, it is difficult to describe the moving tendency of wind speed and accurately forecast it. In our study, a novel EMD–ENN approach, a hybrid of empirical mode decomposition (EMD) and Elman neural network (ENN), is proposed to forecast wind speed. First, the original wind speed datasets are decomposed into a collection of intrinsic mode functions (IMFs) and a residue by EMD, yielding relatively stationary sub-series that can be readily modeled by neural networks. Second, both IMF components and residue are applied to establish the corresponding ENN models. Then, each sub-series is predicted using the corresponding ENN. Finally, the prediction values of the original wind speed datasets are calculated by the sum of the forecasting values of every sub-series. Moreover, in the ENN modeling process, the neuron number of the input layer is determined by a partial autocorrelation function. Four prediction cases of wind speed are used to test the performance of the proposed hybrid approach. Compared with the persistent model, back-propagation neural network, and ENN, the simulation results show that the proposed EMD–ENN model consistently has the minimum statistical error of the mean absolute error, mean square error, and mean absolute percentage error. Thus, it is concluded that the proposed approach is suitable for wind speed prediction. |
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| Keywords: | Wind speed prediction EMD Elman neural network PACF Hybrid model |
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